On Bayesian model and variable selection using MCMC
On Bayesian model and variable selection using MCMC
Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.
gibbs sampler, independence sampler, metropolis–hastings, reversible jump
27-36
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131
2002
Dellaportas, Petros
df8947f6-37ea-4e68-8967-eb43f777a5fd
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879
Ntzoufras, Ioannis
334f2302-b765-48d1-90b6-121ec8e31131
Dellaportas, Petros, Forster, Jonathan J. and Ntzoufras, Ioannis
(2002)
On Bayesian model and variable selection using MCMC.
Statistics and Computing, 12 (1), .
(doi:10.1023/A:1013164120801).
Abstract
Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.
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Published date: 2002
Keywords:
gibbs sampler, independence sampler, metropolis–hastings, reversible jump
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Statistics
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Local EPrints ID: 29965
URI: http://eprints.soton.ac.uk/id/eprint/29965
ISSN: 0960-3174
PURE UUID: df354b04-8013-4058-b3aa-ad8d4ccb8081
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Date deposited: 10 May 2006
Last modified: 16 Mar 2024 02:45
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Contributors
Author:
Petros Dellaportas
Author:
Jonathan J. Forster
Author:
Ioannis Ntzoufras
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